Search Results (1,814)

This study ground in the Innovation Diffusion Theory (IDT), explores the value of digital twin technology in cross-border logistics risk management. Using structural equation modeling, it examines how five innovation characteristics of digital twins—relative advantage, compatibility, complexity, trialability, and observability—influence risk management capabilities, specifically robustness and resilience, within cross-border logistics systems. The findings reveal that relative advantage, compatibility, trialability, and observability significantly enhance both robustness and resilience, while complexity does not show a significant negative impact. Furthermore, the study confirms that improvements in risk management capabilities contribute positively to competitive performance. This research not only enriches the theoretical understanding of digital twin applications in cross-border logistics but also offers valuable insights for practical implementation by enterprises. Full article

As a critical external mechanism driving green innovation, institutional and competitive pressure often coexist and jointly shape firms’ strategic responses. However, existing studies primarily focus on the individual effects of these pressures, with limited attention to their interactive impacts on green innovation. Drawing on optimal distinctiveness theory, this study proposes a “pressure–response” analytical framework that classifies institutional and competitive pressure combinations into congruent (i.e., high–high or low–low) and incongruent (i.e., high–low or low–high) pressure contexts based on their relative intensities. It further examines how these distinct configurations affect two types of green innovation: strategic green innovation (StrGI) and substantive green innovation (SubGI). Using panel data from Chinese A-share listed firms between 2010 and 2022, the empirical results reveal that under congruent pressure contexts, the alignment of institutional and competitive pressures tends to suppress green innovation. In contrast, under incongruent contexts, the misalignment between the two pressures significantly promotes green innovation. Regarding innovation motivation, the high institutional–low competitive pressure context more significantly promotes StrGI, while the low institutional–high competitive pressure context has a more prominent effect on SubGI. In addition, this study also investigates the mediating roles of StrGI and SubGI on ESG performance. The findings provide theoretical support and policy implications for improving green transition policies and institutional frameworks, as well as promoting sustainable corporate development. Full article

With the promotion of national fitness, the requirements for regulating indoor environments during non-competition periods are low and relatively flexible under the trend of composite sports buildings. To maximize the use of natural ventilation and lighting for energy savings, passive optimization design based on building ontology has emerged as an effective strategy. This paper focuses on the spatial prototype of large- and medium-sized gymnasiums, optimizing key geometric design parameters and envelope structure parameters that influence energy consumption. This optimization employs a combination of orthogonal experiments and performance simulations. This study identifies the degree to which each factor affects energy consumption in the competition hall and determines the optimal low-energy consumption gymnasium prototype. The results reveal that the skylight area ratio is the most significant factor impacting the energy consumption of large- and medium-sized gymnasiums. The optimized gymnasium prototype reduced energy consumption by 5.3%~50.9% compared to all experimental combinations. This study provides valuable references and insights for architects during the initial stages of designing sports buildings to achieve low energy consumption. Full article

Emerging markets face growing pressures to integrate sustainable English business practices while maintaining economic growth, particularly in addressing environmental challenges and achieving carbon neutrality goals. English Financial information extraction becomes crucial for supporting green finance initiatives, Environmental, Social, and Governance (ESG) compliance, and sustainable investment decisions in these markets. This paper presents FinATG, an AI-driven autoregressive framework for extracting sustainability-related English financial information from English texts, specifically designed to support emerging markets in their transition toward sustainable development. The framework addresses the complex challenges of processing ESG reports, green bond disclosures, carbon footprint assessments, and sustainable investment documentation prevalent in emerging economies. FinATG introduces a domain-adaptive span representation method fine-tuned on sustainability-focused English financial corpora, implements constrained decoding mechanisms based on green finance regulations, and integrates FinBERT with autoregressive generation for end-to-end extraction of environmental and governance information. While achieving competitive performance on standard benchmarks, FinATG’s primary contribution lies in its architecture, which prioritizes correctness and compliance for the high-stakes financial domain. Experimental validation demonstrates FinATG’s effectiveness with entity F1 scores of 88.5 and REL F1 scores of 80.2 on standard English datasets, while achieving superior performance (85.7–86.0 entity F1, 73.1–74.0 REL+ F1) on sustainability-focused financial datasets. The framework particularly excels in extracting carbon emission data, green investment relationships, and ESG compliance indicators, achieving average AUC and RGR scores of 0.93 and 0.89 respectively. By automating the extraction of sustainability metrics from complex English financial documents, FinATG supports emerging markets in meeting international ESG standards, facilitating green finance flows, and enhancing transparency in sustainable business practices, ultimately contributing to their sustainable development goals and climate action commitments. Full article

Drosophila suzukii is an invasive pest of many fruit crops worldwide. Employing the Sterile Insect Technique (SIT) could mitigate D. suzukii population growth and crop damage. This study evaluated the efficacy of SIT on commercial fruit, by (1) validating the quality of irradiated sterile males (male mating competitiveness, courtship, and flight performance) in the laboratory, and (2) assessing population suppression and fruit damage reduction in commercial raspberry fields. Treatment with SIT was compared to the grower’s standard chemical insecticide program throughout the season. The principal metrics of efficacy were trap counts of wild adult female D. suzukii in crops and larvae per fruit during harvesting. These metrics together with monitoring of border areas allowed targeting of high-pressure areas with higher releases of sterile males, to maximise efficacy for a given release number. The sterile male D. suzukii were as competitive as their fertile non-irradiated counterparts in laboratory mating competitiveness and flight performance studies while fertility egg-to-pupae recovery was reduced by 99%. In commercial raspberry crops, season-long releases of sterile males significantly suppressed the wild D. suzukii population, compared to the grower standard control strategy; with up to 89% reduction in wild female D. suzukii and 80% decrease in numbers of larvae per harvested fruit. Additionally, relative fruit waste (i.e., percentage of harvested fruits rejected for sale) at harvest was reduced for early, mid and late harvest crops, by up to 58% compared to the grower standard control. SIT has the potential to provide an effective and sustainable strategy for managing D. suzukii in raspberries, increasing marketable yield by reducing adult populations, fruit damage and waste fruit. SIT could therefore serve as a valuable tool for integrated pest management practices in berry production systems. Full article

The advancement of computational capabilities has allowed for more efficient airfoil analysis and design. Consequently, it has become possible to expand the design space and explore new geometries and configurations. However, the current state of development does not yet support a fully automated optimization process. Instead, the newly introduced capabilities have effectively transferred the previously trial-and-error-based approach used in geometry design to the formulation of the optimization problem. The goal of this work is to study the formulation of an optimization problem and propose a new methodology that better portrays the aircraft’s requirements for airfoil performance. The new objective function, added to an existing tool, estimates the main performance parameters of an aircraft for the Air Cargo Challenge (ACC) 2022 competition using a method that extrapolates the characteristics of the airfoil into the aircraft’s performance. In addition, the traditional relative aerodynamic property improvements, in this work, are coupled with the performance results to smooth the polar curve of the resulting airfoil. The optimization algorithm is based on the free-gradient technique Particle Swarm Optimization (PSO), using the B-spline parametrization and a coupled viscous/inviscid interaction method as the flow solver. Full article

The article addresses the question of the relative importance of human population size and growth in relation to the environmental problems of planetary heating and biodiversity loss in the current, Anthropocene era. To what extent could policies to encourage lower fertility be justified, while observing that this subject is an inherently contested one. It is proposed that a helpful distinction can be made between specific threats to habitats and biodiversity, as opposed to those related to global energy use and warming. Pressures of over-population can be important in relation to the former. But with regard to the latter—rising per capita energy usage—reduced fertility has historically been positively, not negatively correlated. A case can be made that the high-fertility nations of sub-Saharan Africa could benefit from culturally respectful fertility reduction policies. However, where planetary heating is concerned, it is the hydrocarbon-based, per capita energy-consumption patterns of already low-fertility populations on the other five inhabited continents that is rather more critical. While it will be helpful to stabilise global human population, this cannot be viewed as a solution to the climate crisis problem of this century. That requires relentless focus on reducing hydrocarbon use and confronting the rising inequality since c.1980 that has been exacerbating competitive materialist consumerism. This involves the ideological negotiation of values to promote a culture change that understands and politically embraces a new economics of both human and planetary balance, equity, and distribution. Students of populations can contribute by re-assessing what can be the appropriate demographic units and measures for policies engaging with the challenges of the Anthropocene. Full article

Laser beam powder bed fusion of metals (PBF-LB/M, or simply L-PBF) has emerged as one of the most competitive additive manufacturing technologies for producing complex metallic components with high precision, design freedom, and minimal material waste. Among the various categories of additive manufacturing processes, L-PBF stands out, paving the way for the execution of part designs with geometries previously considered unfeasible. Despite offering several advantages, parts with overhang features require the use of support structures to provide dimensional stability of the part. Support structures achieve this by resisting residual stresses generated during processing and assisting heat dissipation. Although the scientific community acknowledges the role of support structures in the success of L-PBF manufacturing, they have remained relatively underexplored in the literature. In this context, the present work investigated the impact of laser power and scanning speed on the dimensioning, integrity and tensile strength of single-walled block type support structures manufactured in AISI 316L stainless steel. The method proposed in this work is divided in two stages: processing parameter exploration, and mechanical characterization. The results indicated that support structures become more robust and resistant as laser power increases, and the opposite effect is observed with an increment in scanning speed. In addition, defects were detected at the interfaces between the bulk and support regions, which were crucial for the failure of the tensile test specimens. For a layer thickness corresponding to 0.060 mm, it was verified that the combination of laser power and scanning speed of 150 W and 500 mm/s resulted in the highest tensile resistance while respecting the dimensional deviation requirement. Full article

In the context of the growing need to reduce greenhouse gas emissions and to develop sustainable solutions for the construction industry, foamed geopolymers represent a promising alternative to traditional binders and insulation materials. This study investigates the thermal properties of novel low-emission, insulating geopolymer binders made from fly ash with diatomite, chalcedonite, and wood wool aiming to assess their potential for use in thermal insulation systems in energy-efficient buildings. The stability of the foamed geopolymer structure is also assessed. Measurements of thermal conductivity, specific heat, microstructure, density, and compressive strength are presented. The findings indicate that the selected geopolymer formulations exhibit low thermal conductivity, high heat capacity and low density, making them competitive with conventional insulation materials—mainly load-bearing ones such as aerated concrete and wood wool insulation boards. Additionally, incorporating waste-derived materials reduces the production carbon footprint. The best results are represented by the composite incorporating all three additives (diatomite, chalcedonite, and wood wool), which achieved the lowest thermal conductivity (0.10154 W/m·K), relatively low density (415 kg/m³), and high specific heat (1.529 kJ/kg·K). Full article

Range-expanding species, or neonatives, are native plants that spread beyond their original range due to recent climate or human-induced environmental changes. Retama monosperma was initially planted near the Guadalquivir estuary for dune stabilisation. However, changes in the sedimentary regime and animal-mediated dispersal have facilitated its exponential expansion, threatening endemic species and critical dune habitats. The main objective of this study was to identify the key functional traits that may explain the competitive advantage and rapid spread of R. monosperma in coastal dune ecosystems. We compared its seasonal responses with those of three co-occurring woody species, two native (Juniperus phoenicea and J. macrocarpa) and one naturalised (Pinus pinea), at two sites differing in groundwater availability within a coastal dune area (Doñana National Park, Spain). We measured water relations, leaf traits, stomatal conductance, photochemical efficiency, stable isotopes, and shoot elongation in 12 individuals per species. Repeated-measures ANOVA showed significant effects of species and species × season interaction for relative water content, shoot elongation, effective photochemical efficiency, and stable isotopes. R. monosperma showed significantly higher shoot elongation, relative water content, and photochemical efficiency in summer compared with the other species. Stable isotope data confirmed its nitrogen-fixing capacity. This characteristic, along with the higher seasonal plasticity, contributes to its competitive advantage. Given the ecological fragility of coastal dunes, understanding the functional traits favouring the success of neonatives such as R. monosperma is essential for biodiversity conservation and ecosystem management. Full article

(1) Background: Wetlands are ecologically significant ecosystems that support biodiversity and contribute to essential environmental functions such as water purification, carbon storage and flood regulation. However, these ecosystems face increasing pressures from land-use change and degradation, prompting the need for scalable and accurate classification methods to support conservation and policy efforts. In this research, our motivation was to test whether high-spatial-resolution PlanetScope imagery can be used with pixel-based machine learning to support the mapping and monitoring of wetlands at a national scale. (2) Methods: This study compared four machine learning classification models—Random Forest (RF), XGBoost (XGB), Histogram-Based Gradient Boosting (HGB) and a Multi-Layer Perceptron Classifier (MLPC)—to detect and map wetland areas across New Zealand. All models were trained using eight-band SuperDove satellite imagery from PlanetScope, with a spatial resolution of ~3 m, and ancillary geospatial datasets representing topography and soil drainage characteristics, each of which is available globally. (3) Results: All four machine learning models performed well in detecting wetlands from SuperDove imagery and environmental covariates, with varying strengths. The highest accuracy was achieved using all eight image bands alongside features created from supporting geospatial data. For binary wetland classification, the highest F1 scores were recorded by XGB (0.73) and RF/HGB (both 0.72) when including all covariates. MLPC also showed competitive performance (wetland F1 score of 0.71), despite its relatively lower spatial consistency. However, each model over-predicts total wetland area at a national level, an issue which was able to be reduced by increasing the classification probability threshold and spatial filtering. (4) Conclusions: The comparative analysis highlights the strengths and trade-offs of RF, XGB, HGB and MLPC models for wetland classification. While all four methods are viable, RF offers some key advantages, including ease of deployment and transferability, positioning it as a promising candidate for scalable, high-resolution wetland monitoring across diverse ecological settings. Further work is required for verification of small-scale wetlands (<~0.5 ha) and the addition of fine-spatial-scale covariates. Full article

Stock price forecasting remains a persistent challenge in time series analysis due to complex inter-stock relationships and dynamic textual signals such as financial news. While Graph Neural Networks (GNNs) can model relational structures, they often struggle with capturing higher-order dependencies and are sensitive to noise. Moreover, sentiment signals are typically aggregated using fixed time windows, which may introduce temporal bias. To address these issues, we propose DASF-Net (Diffusion-Aware Sentiment Fusion Network), a multimodal framework that integrates structural and textual information for robust prediction. DASF-Net leverages diffusion processes over two complementary financial graphs—one based on industry relationships, the other on fundamental indicators—to learn richer stock representations. Simultaneously, sentiment embeddings extracted from financial news using FinBERT are aggregated over an empirically optimized window to preserve temporal relevance. These modalities are fused via a multi-head attention mechanism and passed to a temporal forecasting module. DASF-Net integrates daily stock prices and news sentiment, using a 3-day sentiment aggregation window, to forecast stock prices over daily horizons (1–3 days). Experiments on 12 large-cap S&P 500 stocks over four years demonstrate that DASF-Net outperforms competitive baselines, achieving up to 91.6% relative reduction in Mean Squared Error (MSE). Results highlight the effectiveness of combining graph diffusion and sentiment-aware features for improved financial forecasting. Full article

In the process of CNC machining, reducing energy consumption, production costs, and improving machining quality are critical strategies for enhancing product competitiveness. Based on an analysis of machine tool processing mechanisms, calculation models for energy consumption, manufacturing cost, and quality (represented by surface roughness) in CNC lathes were established. These models were optimized using the Egret Swarm Optimization Algorithm (ESOA), which integrates three core strategies: waiting, random search, and bounding mechanisms. With the objectives of minimizing energy consumption, manufacturing cost, and maximizing quality, cutting parameters (e.g., cutting speed, feed rate, and depth of cut) were selected as optimization variables. A multi-objective ESOA (MOESOA) framework was applied to resolve trade-offs among conflicting objectives, and the effectiveness of the proposed method was validated through a case study. The simulation results show that the optimization of cutting parameters is beneficial to energy conservation during the machining process, although it may increase costs. Additionally, under the three-objective optimization, the improvement of surface roughness is relatively limited. The further two-objective (energy consumption and cost) optimization model demonstrates better convergence while ensuring that the surface roughness meets the basic requirements. This method provides an effective tool for optimizing cutting parameters. Full article

To investigate the characteristics of rhizosphere soil microbial communities associated with Schisandra sphenanthera across different altitudinal gradients and to reveal the driving factors of microbial community dynamics, this study collected rhizosphere soil samples at four elevations: 900 m (HB1), 1100 m (HB2), 1300 m (HB3), and 1500 m (HB4). High-throughput sequencing and molecular ecological network analysis were employed to analyze the microbial community composition and species interactions. A null model was applied to elucidate community assembly mechanisms. The results demonstrated that bacterial communities were dominated by Proteobacteria, Acidobacteriota, Actinobacteriota, and Chloroflexi. The relative abundance of Proteobacteria increased with elevation, while that of Acidobacteriota and Actinobacteriota declined. Fungal communities were primarily composed of Ascomycota and Basidiomycota, with both showing elevated relative abundances at higher altitudes. Diversity indices revealed that HB2 exhibited the highest bacterial Chao, Ace, and Shannon indices but the lowest Simpson index. For fungi, HB3 displayed the highest Chao and Ace indices, whereas HB4 showed the highest Shannon index and the lowest Simpson index. Ecological network analysis indicated stronger bacterial competition at lower elevations and enhanced cooperation at higher elevations, contrasting with fungal communities that exhibited increased competition at higher altitudes. Altitude and soil nutrients were negatively correlated with soil carbon content, while plant nutrients and fungal diversity positively correlated with soil carbon. Null model analysis suggested that deterministic processes dominated bacterial community assembly, whereas stochastic processes governed fungal assembly. These findings highlight significant altitudinal shifts in the microbial community structure and assembly mechanisms in S. sphenanthera rhizosphere soils, driven by the synergistic effects of soil nutrients, plant growth, and fungal diversity. This study provides critical insights into microbial ecology and carbon cycling in alpine ecosystems, offering a scientific basis for ecosystem management and conservation. Full article

Text2Cypher is a text-to-text task that converts natural language questions into Cypher queries. Recent research by Neo4j on Text2Cypher demonstrates that fine-tuning a baseline language model (a pretrained and instruction-tuned generative model) using a comprehensive Text2Cypher dataset can effectively enhance query generation performance. However, the improvement is still insufficient for effectively learning the syntax and semantics of complex natural texts, particularly when applied to unseen Cypher schema structures across diverse domains during training. To address this challenge, we propose a novel refinement training method based on baseline language models, employing reinforcement learning with Group Relative Policy Optimization (GRPO). This method leverages extracted semantic information, such as key-value properties and triple relationships from input texts during the training process. Experimental results of the proposed refinement training method applied to a small-scale baseline language model (SLM) like Qwen2.5-3B-Instruct demonstrate that it achieves competitive execution accuracy scores on unseen schemas across various domains. Furthermore, the proposed method significantly outperforms most baseline LMs with larger parameter sizes in terms of Google-BLEU and execution accuracy scores over Neo4j’s comprehensive Text2Cypher dataset, with the exception of colossal LLMs such as GPT4o, GPT4o-mini, and Gemini. Full article